Performance Effects of Technological Dynamism: Private vs. State Enterprises in Russia
Bibliographic record
Abstract
How has the privatization-led economic transition transformed Russian firms? Prior to recent economic recession caused by Western sanctions, Russia’s economic performance has been impressive, yet little is known about its micro-level sources. Particularly, while macro-level data suggests a positive effect of privatization, such effect is seldom substantiated at firm-level in Russia. To take a step towards opening the black box, we investigated the performance effect of technology dynamism in Russian firms and the extent to which ownership mattered with regards to the technology dynamism-performance link. Our survey data shows that performance is driven by IT adoption, entrepreneurial orientation, and technological turbulence in Russian firms and that the positive effects of technological turbulence are stronger for private than for state-owned Russian firms. According to our results, Russian private enterprises appear more capable of buffering and gaining from technological turmoil, suggesting that the most significant outcome of organizational transformation in Russia is the firms enhanced capability in managing external environmental dynamism.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".